Energy efficient clustering with compressive sensing for underwater wireless sensor networks

Bhaskarwar, Roshani V.; Pete, D. J.

doi:10.1007/s12083-022-01346-z

Cited by 11 publications

(4 citation statements)

References 52 publications

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“…Additionally, relay node selection based on residual energy and depth reduces the energy consumption required for inter-cluster communication. In [30], an energy efficient underwater wireless sensor networks clustering protocol (EEUCP) is proposed to improve energy efficiency, avoid communication voids, and enhance network quality of service. EEUCP employs the K-means algorithm for clustering and utilizes fuzzy logic for optimal cluster head selection.…”

Section: Related Workmentioning

confidence: 99%

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Mei,

Wang,

Shen

et al. 2024

JMSE

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Clustering protocols for underwater acoustic sensor networks (UASNs) have gained widespread attention due to their importance in reducing network complexity. Congestion occurs when the intra-cluster load is greater than the upper limit of the intra-cluster information transmission capacity, which leads to a dramatic deterioration of network performance despite the reduction of network complexity. To avoid congestion, we propose a node load and location-based clustering protocol for UASNs (LLCP). First, a node load and location-based optimization mechanism is proposed. The number of cluster members is optimized based on node load and location to maximize the number of cluster members while avoiding congestion. Then, a node degree and location-based cluster member selection mechanism is proposed to select the optimal cluster members. Finally, a priority-based clustering mechanism is proposed. The node clustering order is adjusted based on the clustering priority to maximize the reduction of network complexity by increasing the average number of cluster members. Simulation results show that our proposed LLCP minimizes the network complexity while avoiding congestion.

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Section: Related Workmentioning

confidence: 99%

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Mei,

Wang,

Shen

et al. 2024

JMSE

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“…Roshani V. Bhaskarwar [17] have introduced cluster-based routing protocol named as energy efficient UWSN clustering protocol (EEUCP). The sensor nodes of underwater in various layers were separated to clusters by K means.…”

Section: Related Workmentioning

confidence: 99%

Energy Optimized Cluster Head Selection Based on Multi-Objective Sand Cat Swarm Optimization in Under Water Wireless Sensor Networks

2024

IJIES

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Underwater wireless sensor networks (UWSNs) are the kind of wireless sensor networks which is deployed in an underwater environment with the help of physical sensors. UWSN has applications in different fields like the management of disasters, and navigating the underwater environment and marine species. The nodes present in the UWSN do not have an inbuilt battery, so the available energy sources must be used effectively. To retain energy efficiency in UWSN, effective clustering and routing protocols must be utilized in the UWSN environment. But, the cluster-based routing approaches are effective only for conventional wireless sensor networks. Moreover, the problem related to void hole occurs from sensor node and enhances energy dissipation which helps to minimize the life span of UWSN. So, the research proposed multi-objective sand cat swarm optimization (M-SCSO) for the selection of optimistic cluster heads (CH) and provides a better routing path for the transmission of data without a void hole problem. The parameters such as distance between neighbouring nodes, distance between base station and cluster head, residual energy, and node degree are considered while selecting optimistic CHs using MSCSO. Secondly, energyefficient routing is processed using MSCSO to deliver data packets in the shortest path. The results obtained through experimental validation represent effectiveness of proposed method. The proposed approach obtained a better packet delivery rate (PDR) of 99.32% whereas the existing emperor penguin optimized Q learning achieved PDR of 90%.

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“…The city's underlying networked infrastructure may greatly improve a wide range of municipal operations and promote elements of social, cultural, and urban growth via the employment of smart devices, computers, sensors, and actuators. While there has been a rise in interest in the concept and implementation of what we now call "smart cities," the academic literature has paid less attention to the issue, initiatives like Chicago's Array of Things (AoT) project, which is made up of a network of modular [2] and interactive sensor boxes that collect real-time data on the city's environment, infrastructure, and activity for the benefit of research and public use [3]. For this vision of the "smart city" to become a reality, sensors and sensor networks are essential because they can gather and transmit data that may contribute to improving public and commercial services [4].…”

Section: Introductionmentioning

confidence: 99%

Data Aggregation through Hybrid Optimal Probability in Wireless Sensor Networks

Balaji,

Jeevanandham,

Choudhry

et al. 2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: In the realm of Wireless Sensor Networks (WSN), effective data dissemination is vital for applications like traffic alerts, necessitating innovative solutions to tackle challenges such as broadcast storms. OBJECTIVES: This paper proposes a pioneering framework that leverages probabilistic data aggregation to optimize communication efficiency and minimize redundancy. METHODS: The proposed adaptable system extracts valuable insights from the knowledge base, enabling dynamic route adjustments based on application-specific criteria. Through simulations addressing bandwidth limitations and local broadcast issues, we establish a robust WSN-based traffic information system. RESULTS: By employing primal-dual decomposition, the proposed approach identifies optimal packet aggregation probabilities and durations, resulting in reduced energy consumption while meeting latency requirements. CONCLUSION: The efficacy of proposed method is demonstrated across various traffic and topology scenarios, affirming that probabilistic data aggregation effectively mitigates the local broadcast problem, ultimately leading to decreased bandwidth demands.

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Energy efficient clustering with compressive sensing for underwater wireless sensor networks

Cited by 11 publications

References 52 publications

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Energy Optimized Cluster Head Selection Based on Multi-Objective Sand Cat Swarm Optimization in Under Water Wireless Sensor Networks

Data Aggregation through Hybrid Optimal Probability in Wireless Sensor Networks

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